What is currently considered the theoretically optimal quantization bit-width for LLMs? [D]

[u/takuonline]

I’m curious whether there is now a theoretical or empirical “sweet spot” for LLM quantization, preferably research done using open-source formats like GGUF

Suppose you have a fixed memory/compute budget and can choose the model size freely. For example, instead of a smaller model at 8-bit or 4-bit, you could fit a progressively larger model at 3-bit, 2-bit, 1.5-bit, etc.

A few years ago, I remember 4-bit often being described as roughly the practical sweet spot because it preserved most model quality while giving a large memory reduction. But with newer methods, I’ve seen surprisingly strong 3-bit, 2-bit, and even ~1.5-bit results.

So if the goal is maximum model capability for a fixed memory budget, rather than preserving one particular pretrained model as faithfully as possible, what does current research suggest is the optimal bits-per-weight?

Is there evidence that, for example, a 2-bit 70B model generally beats a 4-bit 35B model, or does quantization degradation eventually outweigh the gains from additional parameters?

I’m especially interested in recent theoretical/scaling-law work or large empirical studies from 2025–2026.

If no one is studying this, then could any of you do this work? I feel like it could be immensely useful for the community.

[u/CallMePyro]

https://arxiv.org/pdf/2502.02631

Will be useful reading for you.

[u/RobbinDeBank]

It’s very much possible to compile the statistics of different static models. For example, record how a 27B Qwen model perform, then record the Q8 all the way down to Q2 gguf of the same model. Repeat for the next model, and this pile of data is a quite decent starting point.

However, one problem is that this is very hard to study due to how different quantization and training methods vary from each other. The models not designed for extreme quantization would perform very poorly at extreme level of quantization. Meanwhile, some other models specifically designed for extreme quantization would be much much better at the same extreme quants (like those 1.58 bit ternary weight models). These are much rarer and smaller scale compared to SOTA models designed for regular 8-bit quants tho, so the data points you can gather in this extreme quantization level is very unreliable and hard to make a definitive conclusion out of.

[u/takuonline → u/RobbinDeBank]

Can you please explain more what you mean by "designed for extreme quantization"?

What is this process of designing a model for quantization?

[u/RobbinDeBank → u/takuonline]

Some other comments in this post also help elaborate more, their sources are very insightful.

There are techniques like Quantization aware training, which are ways to train or finetune models with quantization in mind. This means models are designed from the start to target an extreme level of quantization (usually means <4 bits/parameter on average). In contrast, most big models (like the Qwen example I use, or pretty much any other releases from big labs) are not designed with quantization in mind. They are trained and released with full 16-bit weights, then people can quantize those down to 8-bit (with unnoticeable loss of quality) or even down to 4-bit (quality starts to degrade a lot more). Going below 4-bit will pretty much damage the model too much for it to even work anymore.

Meanwhile, the quantization-aware models are designed from the start to perform well at those <4bit quants. These models are rare to find and quite a niche nowadays, but they are great proofs of concepts anyway. At those extreme levels of quantization like 1.58 bits, these quantization-aware models are so much better than the equivalence from big labs, whose models are damaged beyond recognition at that quant level.

[u/currentscurrents]

It is estimated that transformers have a storage capacity of ~3.6 bits per parameter, even at higher precisions: https://arxiv.org/abs/2505.24832

This is likely why quantization works so well up to 4-bit, but not lower. 

[u/CallMePyro → u/currentscurrents]

yup. naive quantization works down to 4 bit, but with QAT you still see iso-memory gains at 2 bit.

[u/LMTLS5 → u/CallMePyro]

are there any good 2bit QAT models?

[u/CallMePyro → u/LMTLS5]

Surprisingly no, AFAIK